Free-Energy Based Modeling of Planar Dielectric Elastomer Actuators
Bibliographic record
Abstract
Dielectric elastomer actuators (DEAs) have gained increasing attention over the last decades and have been widely developed for applications fields such as robots, aerospace, biomedicine due to the fast response, high energy density, light weight, and low cost. However, the task of modeling of DEAs is typically challenged in the presence of the nonlinear features, time-independent viscoelastic behaviors, complex electromechanical coupling, etc. \nTo address such a challenge, a free-energy based model for DEAs moving in vertical direction is proposed, in an effort to investigate the physical properties of DEAs in this research. The developed model is based on the principle of nonequilibrium thermodynamics, where the Gent model and generalized Maxwell model are applied to describe the free energy and viscoelastic behavior of DEA, respectively. Unlike the existing modeling methods, this research narrows the focus on the inertial force and viscoelasticity which leads to DEA’s instability. \nAfter that, the free-energy based model is simulated in MATLAB and the several sets of experiments are implemented by setting various driving voltage amplitudes and frequencies. According to the experimental data, the undetermined parameters of the model are identified by using differential evolutionary algorithm. The comparison of the model simulation and experimental results supports the validation of the proposed free-energy based model.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".